Dietary specialization among individual squid: using Illex argentinus as a case and meta-analysis for other squid species

Abstract

Squid, which occupy a similar role to teleost fish as open water predators, are opportunistic foragers. However, there is a lack of empirical evidence related to whether individual squid specialize their diets to optimize fitness. We investigated whether individual squid have specialized diets and what factors impact any specialization using the Argentine shortfin squid as a case study species, coupled with a meta-analysis for other squid species. Hutchinson’s n-dimensional hypervolume concept was used to estimate individual dietary niches based on stable isotope and fatty acid analyses. Individual squid showed a high degree of dietary specialization, with individual specialization indices typically greater than 0.70, and pairwise niche overlap less than 0.5, with adults having greater specialization in dietary niche. For the Argentine shortfin squid, higher reproductive investment and water temperature increased individual dietary specialization. Individual dietary specialization probably reduces interindividual competition, optimizes food resource use and increases fitness and hence net energy gain for reproduction. The existence of dietary specialization at the individual level provides insight into the life history of squid.

1. Introduction

Squid, a marine mollusc with a distinct head and a set of eight arms and two tentacles surrounding the mouth, are an important component of marine ecosystems, occupying a similar role to teleost fish as open water predators [1]. Squid have short lifespans, usually 1−2 years, grow fast and spawn once before dying [2]. Squid support among the most important marine fisheries, and annual catches account for around 3.4% of global marine production [3]. The biomass of squid is closely related to effective fecundity, which depends on energy allocation to reproduction [4]. Squid have evolved a voracious and opportunistic feeding strategy to acquire energy, feeding on any prey items that they encounter, including other squid and even exhibiting conspecific feeding [1]. Thus, it might be expected that squid are generalist feeders and have a wide dietary niche.

However, a wide dietary niche often leads to competition among conspecifics or with other species, due to the limited resources within a given habitat [5–7]. In general, the niche occupied by a species is the union of the niches for individuals [8,9]. Thus, generalist species, i.e. those with a broad dietary niche, can consist of generalist and specialist individuals, where generalist individuals have a dietary niche that approximates that for whole species, while the dietary niches for specialist individuals are narrow. Generalist species with specialist individuals are referred to ‘generalist specialists’, defined as having the ability to both generalize at the species level and specialize at the individual level in an environment [10,11]. While the phenomenon of individual variation in dietary niches is acknowledged widely among ecologists (e.g. [11,12]), empirical support for individual dietary niches of squid species is still lacking.

Stable isotopes and fatty acids are increasingly used to explore the feeding habits and trophic niches of organisms within a given ecosystem [13,14]. These biochemical compounds are metabolized and assimilated by consumers in a predictable manner, with the patterns in the fatty acid and isotopic signatures of their tissues reflecting their diet. For example, stable carbon isotopes (δ¹³C) show little change in the ¹³C/¹²C ratio with trophic transfer between prey and predators [15,16]. They are therefore useful as tracers for dietary sources of carbon within a food web, indicating the foraging areas of predators [17–19]. In contrast, stable nitrogen isotopes (δ¹⁵N) are enriched by 2−5‰ with each trophic level, indicating the consumer’s trophic position [20,21]. Isotopic signatures have been shown to be sequentially deposited in the hard tissues of squid (e.g. beaks, gladius), recording changes in dietary niche over entire lifetimes [22,23]. Stable isotopic analyses of soft tissues with different turnover rates can also be used to test for the change of foraging niche over time [24,25]. Most fatty acids can only be synthesized by low trophic level species and become essential dietary components for high trophic level species with minimal modification and no degradation [26,27]. Three long-chain polyunsaturated fatty acids, EPA (eicosapentaenoic acid, 20:5n3), DHA (docosahexaenoic acid, 22:6n3) and ARA (arachidonic acid, 20:4n6) in particular have been identified as essential fatty acids for marine animals, and can be used to identify first-order carnivores, second-order carnivores and top predators, respectively [28–30].

We used stable isotope (δ¹³C and δ¹⁵N) and essential fatty acid (20:4n6, 20:5n3 and 22:6n3) data from the digestive gland, mantle muscle and gonad of the Argentine shortfin squid (Illex argentinus) to investigate individual dietary specialization during maturation. Illex argentinus is one of the most important targeted species in global cephalopod fisheries, contributing around 12% of global capture of cephalopods annually [3] due to its large biomass, and it also plays a key role as a transient ‘biological pump’ between the South Atlantic Subtropical Gyre, Patagonian Shelf and Polar Frontal Zone in the southwest Atlantic [31]. In general, the biological signatures in the digestive gland of squid, a fast turnover rate tissue, can reflect those of their diet within 10 days [24,25], while muscle tissue has a slower turnover rate that reflects diet over a longer period of time (~4 weeks or longer [24]). Illex argentinus adopts a mixed income–capital breeding strategy, in that reproduction relies primarily on direct food intake, coupled with the use of storage reserves in muscle tissues during maturation [32]. The isotopic values and fatty acids of the gonad may consequently reflect the medium-term fluctuations of food intake. Thus, using biological signatures from multiple tissues allows shifts in diet to be identified as the turnover rate varies among tissues [24,33], and these biological markers have been used to identify dietary selection in the past [34–36]. In addition, we compiled sequential isotopic data from peer-reviewed publications that conducted stable isotope analysis of the hard tissues (beaks, gladii and eye lens) of squid species to further evaluate feeding habits at the individual level.

The data were used to understand the individual trophic niche of squid, especially subadults and adults that increase energy allocation for reproduction substantially [32,37]. We used the Hutchinson’s n-dimensional hypervolume to evaluate individual trophic niches [38], and hypothesized that squid would specialize in diet to meet energy demands at the individual level to support growth and maturation. Our specific aims were to: (i) estimate the trophic niche to evaluate individual dietary selection and (ii) examine the effects of reproductive investment and oceanographic conditions on individual dietary specialization. These results provide insights into the feeding ecology of squid, and hence an improved understanding of intraspecific competition and their ecological and evolutionary consequences.

2. Methods

(a). Argentine shortfin squid

(i). Data collection and sample preparation

Argentine shortfin squid (I. argentinus) were obtained from the catches of a commercial jig vessel in the high seas of the southwest Atlantic Ocean, during January–March 2019. The sampling area was the Patagonian continental shelf (61°09′ W–62°53′ W, 46°08′ S–47°51′ S; figure 1), an important habitat for this species [39]. The sampling procedure involved one sample per fishing location, with specimens only collected on the first fishing day at each location. Each specimen was randomly collected onboard and immediately frozen at −30℃. The specimens were shipped to the laboratory for further analysis within 3–5 months after collection.

Figure 1.

Sampling stations in the southwest Atlantic Ocean and size information for Argentine shortfin squid (I. argentinus). (a) Samping area. Black crosses indicate the sampling stations, and solid lines are isobaths: 100 m (white), 200 m (grey), 500 m (blue) and 1000 m (black). (b) ML distribution. (c) The relationship between BW and ML. BW, body weight; ML, mantle length.

After defrosting in the laboratory, the dorsal mantle length (ML, ±1.0 mm), body weight (BW, ±1.0 g), eviscerated weight (EW, ±1.0 g), sex and maturity stage were recorded for each specimen. Maturity stage was assigned using the macroscopic scale proposed by Lin et al. [40]: (I) immature, (II) developing, (III) physiologically maturing, (IV–V) physiologically mature, (VI) functionally mature, (VII) spawning and (VIII) spent. Thus, the resulting dataset consisted of 66 females (14 at stage III, 14 at stage IV, 13 at stage V, 14 at stage VI and 11 at stage VII; electronic supplementary material, table S1). Preliminary analyses indicated that ML was unimodal, and that BW was related to ML (r² = 0.64; figure 1).

The ventral mantle muscle (~10.0 g wet weight), digestive gland and reproductive system, including the ovary, nidamental glands and oviducts (including any eggs) were weighed to the nearest 0.1 mg and separately lyophilized at −50℃ to a constant weight in a freeze-dried chamber (Scientz-10N lab lyophilizer, Ningbo Scientz Biotechnology Co., Ltd.). Each tissue was ground into fine powder after the dry weight (DW) was measured to the nearest 0.1 mg and used for energy density (ED) analysis and fatty acid analysis.

(ii). Laboratory analysis

Approximately 3.0 g of each powdered tissue (mantle muscle, digestive gland, ovary, nidamental glands and oviducts) was used to determine ED (kJ g⁻¹) using an automatic isoperibol calorimeter (Parr 6400, Parr Instrument Company, Moline, IL, USA). The energy accumulation of each tissue (EA, kJ) was calculated as ED multiplied by wet weight and the dry/wet ratio. The total energy accumulation for a given individual was assumed to be the sum of the energy in each tissue, with the relative reproductive energy estimated as the ratio of the energy of the reproductive tissues to the total energy, expressed as a percentage. Thus, the total energy and relative reproductive energy were calculated using the following formulas:

$${\displaystyle {\displaystyle TE=\sum E{A}_i,}}$$ (2.1)

$${\displaystyle RE=\frac{E_{ova}+E_{nid}+E_{ovi}}{TE}\times 100\mathrm{\%},}$$ (2.2)

where TE is the total energy accumulation; RE is the relative reproductive energy; EA_i is the energy accumulation of tissue i (i.e. mantle muscle, digestive gland, ovary, nidamental glands or oviducts); and E_ova, E_nid and E_ovi are the energy accumulation of the ovary, nidamental gland and oviducts.

The mantle muscle, digestive gland and ovary were selected for stable isotope analysis. The lipid fraction in the soft tissues can be depleted in ¹³C [41,42], so lipid was extracted from the powdered tissue before stable isotope analysis, using a mixture of chloroform : methanol : distilled water (5 : 10 : 4, v/v/v). The lipid was stored for fatty acid methyl esters (FAME) analysis.

After lipid extraction, the tissues were freeze-dried again at −50℃ for 36 h and ground to a fine powder for stable isotope analysis. Approximately 0.2 mg of the lipid-extracted tissue was used to determine the δ¹³C and δ¹⁵N values using a SerCon Integra 2 integrated elemental analyser and an isotope ratio mass spectrometer at the Stable Isotope Laboratory in the Third Institute of Oceanography (Ministry of Natural Resources, China). Isotope ratios were reported in standard δ-notion in parts per thousand (‰) relative to the international standards (Vienna Pee Dee Belemnite and atmospheric nitrogen for carbon and nitrogen isotopes, respectively). The relative differences (δ) of ¹³C and ¹⁵N of the samples were calculated using the equation: [R_sample/R_standard−1] × 1000, where R_sample and R_standard are the ¹³C/¹²C or ¹⁵N/¹⁴N ratios of the sample and the standard. Measurement errors were approximately 0.02‰ and 0.02‰ for δ¹³C and δ¹⁵N, respectively.

FAME were analysed separately for each tissue to determine the fatty acid content at Shanghai Ocean University, based on a modification of the ‘Determination of total fat, saturated fat and unsaturated fat in foods—hydrolytic extraction-gas chromatography’ protocol [43]. The lipids of each tissue, after content determination, were immediately subjected to FAME analysis to minimize contamination and oxidation.

FAME were identified and quantified using an Agilent 7890B Gas Chromatography (GC) coupled to a 5977A series mass spectrometer detector (Agilent Technologies, Inc. USA) with the fatty acid 19 : 0 used as an internal standard. The separation was carried out with helium as the carrier gas, a thermal gradient programmed from 125 to 250℃, and an auxiliary heater at 280℃. The total content of fatty acids is reported as the dry tissue weight (mg g⁻¹ DW⁻¹), and the content of individual fatty acids is expressed as percentages of the total content of fatty acids. Three essential fatty acids (20:4n6, 20:5n3 and 22:6n3) were selected for computing the specialization metrics.

(iii). Statistical analyses

All statistical analyses were performed in R 4.3.0 [44], and statistical significance was defined as p < 0.05. Differences in stable isotopes (δ¹³C and δ¹⁵N), essential fatty acids (20:4n6, 20:5n5 and 22:6n3) and the individual specialization indices (estimated following) among maturity stages were evaluated by first checking for normality using the one-sample Kolmogorov–Smirnov test. One-way ANOVA was then applied to test for differences where the normality was satisfied. Data were analysed using a Kruskal–Wallis non-parametric test when normality and/or homoscedasticity were rejected.

(iv). Analysis—niche estimation and specialization metrics

The relationship between stable isotopes and essential fatty acids and sampling stations was examined using multiple response generalized linear mixed models (MGLMM) employing Bayesian Markov Chain Monte Carlo estimation using the R package MCMCglmm [45] to determine potential spatial effects on the individual niche estimation. δ¹⁵N and δ¹³C or the three essential fatty acids (20:4n6, 20:5n3 and 22:6n3) were multivariate responsive variables, and sampling latitude and longitude were explanatory variables. Sampling months were modelled as random effects in all models to account for the temporal structure in the data and potential differences among sampling stations. The MGLMM results showed insignificant relationships between the stable isotopes and sampling latitude and longitude (electronic supplementary material, tables S2–S4), as well as between the essential fatty acids and sampling latitude and longitude (electronic supplementary material, tables S5–S7), indicating the same resources were available to the individual squid over the sampling area during January–March 2019. Therefore, the estimates of niche volumes and specialization metrics were not considered to reflect the spatial effects.

We estimated individual trophic niches as hypervolumes using stable isotopes and essential fatty acids separately. The carbon and nitrogen stable isotopes were z-scored when estimating the hypervolumes, while the essential fatty acids were scaled using classical multi-dimensional scaling (MDS) [46], and we used the first two MDS dimensions to calculate the hypervolumes. The hypervolumes were constructed using a kernel density approach that estimates the geometric shape of the niche in n-dimensional space [38]. The kernel density approach was conducted using the hypervolume_gaussian function in the Hypervolume package in R [38]. We calculated the holes for the hypervolumes following the framework of Blonder [47] prior to estimating the hypervolumes. The ratio of volumes between detected holes and convex expectation (V_holes/V_convex) for each hypervolume for the carbon and nitrogen stable isotopes was small (0.027 ± 0.002; electronic supplementary material, figure S1a), indicating that approximately 0.027^1/2 = 16% of each axis was unoccupied. The ratio of V_holes/V_convex for the essential fatty acids was also small (0.026 ± 0.003; electronic supplementary material, figure S1b). Therefore, it is reasonable to use the stable isotopes and essential fatty acids to calculate the hypervolumes based on kernel density estimation. We then estimated the specialization index as one minus individual niche volume divided by the species niche volume, which is the union of all individual niches [38]. The specialization index ranges from 0 (generalist) to 1 (specialist) [48].

We also estimated the pairwise niche overlap using the Jaccard overlap metric (Vol(A ⋂ B)/Vol(A ⋃ B), where Vol(A ⋂ B) is the niche intersection of pairwise individuals, and Vol(A ⋃ B) is the niche union of pairwise individuals [38]. The overlap value approaches 0 when the niches for two individuals are totally separate and 1 when they are identical.

The specialization index by maturity stage and niche overlap from pairwise maturity stages was also calculated to further understand individual dietary specialization for the squid.

(v). Analysis—quantifying effects on individual specialization

We used linear mixed-effects models (LMMs; the lmer function in the lme4 package for R [49]) to quantify the potential drivers of individual dietary specialization. Illex argentinus increases energy accumulation for reproduction after the commencement of maturation [32,37], but growth is sensitive to factors such as water temperature, salinity and food availability [2]. Illex argentinus is also found in deeper water after maturation [2], which could impact the available prey. Thus, we analysed the following potential predictors of niche overlap: relative reproductive energy, sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), sea surface chlorophyll a concentration (Chla), ocean mixed layer thickness (MLD) and bottom water temperature (BT). We downloaded these oceanographic variables from the Copernicus Marine Service (https://marine.copernicus.eu), using the locations of the sampling stations for each individual, where SST, SSS, SSH, MLD and BT were obtained from the Global Ocean Physics Analysis and Forecast product (doi: 10.48670/moi-00016), while Chla was from the Global Ocean Biogeochemistry Hindcast product (doi: 10.48670/moi-00019). Maturity stage was considered as a random effect in the LMMs to account for sexual development and potential differences in energy allocation among individuals. All the key predictors were z-scored to maintain comparability among individuals.

The LMMs were based only on the isotopic data as there was a significant linear relationship between the individual specialization indices estimated using the isotopic data and essential fatty acids (electronic supplementary material, figure S2). We used the function buildmer in the package buildmer (v. 2.11) in R [50] to select the optimal LMMs. This function identifies the maximal model that can converge and then performs backward stepwise elimination using a variety of criteria, such as the change in log-likelihood or its significance, Akaike’s information criterion, the Bayesian information criterion and the explained deviance (https://CRAN.R-project.org/package=buildmer) to identify an optimal model.

(b). Meta-analysis

Search engines (Google Scholar and Web of Science) were used to identify peer-reviewed publications that reported results on sequential stable isotopes for hard tissues of squid (beaks, gladii or eye lens). We then selected those papers containing data for individuals (outlined in graphics or electronic supplementary materials), extracted those data using the digitize function (package digitize v. 0.0.4) in R [44] and evaluated individual dietary selection using Hutchinson’s n-dimensional hypervolume as described above.

3. Results

(a). Argentine shortfin squid

(i). Stable isotopes

There were significant differences in δ¹⁵N (Kruskal–Wallis test, χ² = 130.89, p < 0.001) and δ¹³C (ANOVA, F = 218.30, p < 0.001) among the overall analysed tissues. The overall δ¹⁵N values spanned about one trophic level (2.83‰), 11.94 ± 0.57‰ (digestive gland) to 14.77 ± 0.54‰ (mantle muscle), and the overall δ¹³C values spanned 2.33‰, −18.83 ± 0.83‰ (digestive gland) to −16.50 ± 0.46‰ (mantle muscle) (figure 2a). The δ¹⁵N and δ¹³C values varied among individuals (figure 2a). The average δ¹⁵N value within an individual ranged from 12.94‰ to 14.81‰, and the associated standard deviation from 0.82‰ to 2.72‰. The average δ¹³C value within an individual ranged from −18.88‰ to −16.25‰, and the associated standard deviation from 0.49‰ to 2.03‰. The kernel density estimation distribution of isotopes for each individual had different areas of highest density, compared with all individuals combined (figure 2a). There was enrichment of nitrogen stable isotopes during sexual development, evidenced by the significant increase in δ¹⁵N values for the digestive gland (ANOVA, F = 5.65, p = 0.0006) and ovary (ANOVA, F = 7.46, p < 0.05), with increasing maturation stage (figure 2b,c).

Figure 2.

The stable isotopes (δ¹³C and δ¹⁵N) of female Argentine shortfin squid, I. argentinus. (a) The δ¹³C and δ¹⁵N values for each individual squid (grey points) and analysed tissues (black points). The values are represented as means ± s.d. The kernel density distributions of δ¹⁵N and δ¹³C for each individual (grey lines) are shown, respectively, at the right and top of (a), along with the mean distributions for all individuals (bold red and blue lines). (b) δ¹⁵N distribution for the digestive gland by maturity stage. (c) δ¹³C distribution of the ovary by maturity stage.

The specialization index of each squid was estimated to range from 0.43 to 0.89 (0.73 ± 0.10), and was larger than 0.70 for the majority (72.73%) of individuals (figure 3a). Individuals at maturation stage IV and higher had a significantly higher degree of dietary specialization, compared with the physiologically maturing individuals (ANOVA, F = 3.37, p = 0.014; figure 3b). The pairwise overlap between any two individuals varied from 0.27 to 0.84 (0.54 ± 0.14), and 60% of individuals had pairwise niche overlaps with others larger than 0.5, accounting for 62.03% of the overall variance (figure 3c). Overlap was larger for those individuals at maturation stages IV and higher (figure 3d).

Figure 3.

Individual specialization index and pairwise niche overlap based on stable isotope data for Argentine shortfin squid. (a) Distribution of the individual specialization indices. Coloured bars represent: red < 0.45, green < 0.60, blue < 0.75 and purple < 0.90. (b) Specialization index by maturity stage. (c) Distribution of pairwise niche overlap among individuals. (d) Pairwise niche overlap between each two maturity stages. The blue solid lines in (b) and (d) represent a loess smooth through the observations, with 95% confidence intervals in grey shade.

(ii). Essential fatty acids

The 20:4n6 values varied from 0.13% to 2.38% (1.11 ± 0.43%), the 20:5n3 values from 5.82% to 18.35% (11.27 ± 1.92%) and the 22:6n3 values from 12.54% to 37.93% (27.63 ± 6.17%) among individuals. The values for these fatty acids differed significantly among the analysed tissues (20:4n6, ANOVA, F = 6.73, p = 0.0016; 20:5n3, Kruskal–Wallis test, χ² = 30.05, p < 0.05; 22:6n3, Kruskal–Wallis test, χ² = 121.91, p < 0.05), and they varied among individuals (figure 4a–c). The individual specialization index ranged from 0.54 to 0.97 (0.79 ± 0.11) among individuals, with the index for most individuals (75%) > 0.70 (figure 4d). In addition, the pairwise overlap analysis revealed that the pairwise niche overlap ranged from 0 to 0.78 (0.30 ± 0.18), and 84.30% of the individuals had a pairwise overlap value <0.5 (figure 4e).

Figure 4.

The essential fatty acids of female Argentine shortfin squid and the estimated specialization metrics. (a–c) Kernel density distributions for 20:4n6, 20:5n3 and 22:6n3. Grey lines represent the distribution for each individual, and the bold red lines are the mean distributions. (d) Distribution of individual specialization indices. Coloured bars represent: red < 0.55, green < 0.7, blue < 0.85 and purple < 1.0. (e) Distribution of pairwise niche overlap for each pair of individuals.

(iii). Potential effects on individual dietary specialization

The optimal LMMs included reproductive energy, SST and bottom temperature (electronic supplementary material, table S8), and these environmental variables were related significantly to the extent of individual dietary specialization of I. argentinus (table 1). The negative relationship between the specialization index and reproductive energy suggests that dietary specialization is greater when larger amounts of energy are allocated to reproduction (figure 5a). The results of the LMM also indicate that dietary specialization increases with SST and bottom temperature (figure 5b,c).

Table 1.

Results of the LMMs for the individual specialization index in relation to reproductive energy, SST and bottom temperature for Argentine shortfin squid.

Effect item statistics random effects
groups	name	variance	s.d.
maturity stage	(intercept)	0.0001	0.0001
residual		0.0038	0.0614
fixed effects
	estimate	s.e.	t value	Pr(>|t|)
(intercept)	0.7477	0.0076	98.227	2×10–16
reproductive energy	0.0230	0.0085	2.674	0.0009
SST	0.0377	0.0124	3.035	0.0035
bottom temperature	0.0292	0.0119	2.453	0.0168

Figure 5.

Relationships between the individual specialization index and reproductive energy, SST and bottom temperature for Argentine shortfin squid. β is the slope estimated from the optimal LMM. The solid blue line depicts the model fit, with 95% confidence intervals in grey shade.

(b). Meta-analysis

Data were obtained on the sequential isotopic signatures from beaks of giant warty squid (Moroteuthopsis longimana) [23] and Boreoatlantic armhook squid (Gonatus fabricii) [51], from gladii of Humboldt squid (Dosidicus gigas) [52,53], orange-back squid Sthenoteuthis pteropus [54] and Japanese flying squid (Todarodes pacificus) [55], and from eye lenses of diamond squid (Thysanoteuthis rhombus) [56]. A total of 135 individuals had at least three sections of hard tissues available for stable isotope analysis (electronic supplementary material, table S9) and were used for niche estimation and specialization metric analysis.

The distribution of kernel density estimates indicated that isotope values vary among D. gigas, G. fabricii, M. longimana, S. pteropus, T. rhombus and T. pacificus individuals (electronic supplementary material, figure S3). The individual specialization indices were estimated to range from 0.41 to 0.98, with each species having an average index ≥0.67, and the pairwise niche overlap ranged over 0–0.78, with an average value ≤0.29 for each species (table 2). Adults had individual specialization indices greater than 0.67 on average for all species, and also small values of pairwise niche overlap, particularly for M. longimana (figure 6b). Adult G. fabricii had greater individual specialization indices and smaller pairwise niche overlap than subadults, and similarly, adult T. pacificus had greater individual specialization indices than the juveniles and subadults though they exhibited the similar pairwise niche overlap (figure 6).

Table 2.

Summary of individual specialization indices and pairwise niche overlap for the meta-analysed squid species.

species name	species abbreviation	n	individual specialization index		pairwise niche overlap
			range	mean ± s.d.	range	mean ± s.d.
Dosidicus gigas	DGI	50	0.41–0.98	0.86 ± 0.14	0.00–0.71	0.13 ± 0.14
Gonatus fabricii	GFA	14	0.48–0.91	0.72 ± 0.14	0.00–0.71	0.28 ± 0.14
Moroteuthopsis longimana	MLO	4	0.52–0.84	0.73 ± 0.14	0.00–0.15	0.03 ± 0.06
Sthenoteuthis pteropus	SPT	6	0.44–0.93	0.67 ± 0.19	0.09–0.51	0.22 ± 0.12
Thysanoteuthis rhombus	TRH	37	0.44–0.95	0.79 ± 0.14	0.00–0.78	0.29 ± 0.15
Todarodes pacificus	TPA	24	0.45–0.98	0.83 ± 0.10	0.00–0.75	0.24 ± 0.25
pooled		135	0.41–0.98	0.81 ± 0.15	0.00–0.78	0.19 ± 0.18

Figure 6.

Individual specialization indices and pairwise niche overlap of six squid species. (a) Individual specialization index for each squid individual clustered by maturity state. Values are presented as mean ± s.d. The colour bars represent: dark grey = unknown maturation state, orange = juveniles, blue = subadults, red = adults. (b) Boxplot of pairwise niche overlap of individual squid. The white circles represent mean values at each maturity state, and the grey points are the raw data. See table 1 for species abbreviations.

4. Discussion

Squid have long been recognized as generalist feeders. Our results, based on Hutchinson’s n-dimensional hypervolume concept, coupled with re-analysed results from peer-reviewed publications support the hypothesis that there is individual dietary specialization among squid, with a greater degree of dietary specialization for adults. In addition, the individual dietary specialization of I. argentinus is closely related to the energy demand for reproduction and water temperature. These findings provide insights into the feeding ecology of squid, that is dietary specialization at the individual level could be a common way to obtain energy and optimize fitness for squid.

Our results support the hypothesis that most squid species exhibit individual dietary specialization, similar to observations already noted for the Humboldt squid [52,57]. Illex argentinus has low individual specialization indices based on stable isotope and fatty acid data (0.73 and 0.79 on average, respectively). Other squid species also have individual specialization indices larger than 0.67 on average (table 1). These observations indicate that individual squid actively select among alternative prey, leading to narrow dietary niches compared with the species niche. Squid have been found to vary substantially in feeding patterns to optimize food intake, including ontogenetic diet shifts [58], size-dependent predation [59] and migration [19]. Some species also compete for calorific prey items and have a phenotypic response to prey availability [6]. These feeding behaviours could be a consequence of intense intra- and interspecific competition, leading to a large extent of among-individual variation in diet [48]. These squids may tend to specialize in diet to maximize population/species fitness [60], as specialization in diet would not only reduce competition between individuals [8] and maximize the energy intake of consumers [57] but also increase the carrying capacities of prey by increasing predator–prey stability [61,62].

Dietary specialization at the individual level is supported by the results of the pairwise overlap between individuals. In general, overlap will be expected when individuals feed on the same prey species. The meta-analysis using six squid species revealed that most of the individuals have an average pairwise niche overlap value of less than 0.5, and similar results were obtained for the specialization metrics based on essential fatty acids for Argentine shortfin squid (figure 4e). Over 60% of the individual Argentine shortfin squid were estimated to have a pairwise niche overlap value >0.5 using stable isotope data, and individuals at a more advanced maturation state had larger values of pairwise niche overlap, although the specialization indices were large (figure 3). Functional physiological processes such as energy demands for reproduction may be the key drivers influencing individual dietary specialization. It is well documented that gonad development in squid is delayed relative to growth in size, but more energy is required to support individual fitness and reproduction after the commencement of maturation [1], and can be up to 25 times of that accumulated before maturation [37]. Recent studies have found that the reproduction of Argentine shortfin squid and Humboldt squid (D. gigas) depends mainly on food intake [32,63]. It is therefore reasonable to expect that the squid at advanced maturation stages would prefer to feed on high trophic prey because organisms at higher trophic levels have greater ED and can provide more caloric content [64,65].

Squid are characterized by ontogenetic migration to deeper waters where competition for food would be much more intensive as fewer resources are available [1,2]. There is generally a behaviourally based dietary polymorphism among individuals at lower resource abundance, in which they benefit from improved foraging efficiency on their individually preferred prey [66]. Thus, individuals at an advanced maturation state may exhibit a higher degree of specialization in diet. Although we cannot identify the exact prey species consumed by these individuals based on isotopic values, the pairwise overlap among the more mature individuals may be attributable to their focus on high calorific prey, resulting in a high degree of specialization. This conclusion is partly supported by the interindividual variation in the relative content of 20:4n6, 20:5n3 and 22:6n3 for the Argentine shortfin squid (figure 4a–c), as well as the low pairwise niche overlap based on these fatty acids (figure 4e). This is because 20:4n6, 20:5n3 and 22:6n3 have been identified as tracers for top predators, first-order carnivores and second-order carnivores [30]. Specifically, these fatty acids have been identified as essential nutrients for the successful development of embryos and larvae of cephalopods, and can only be supplied through feeding [29,67]. Accordingly, nutritional needs may be another important driver of dietary specialization of individual squid.

Using Argentine shortfin squid as a study system, our results support that there is a significant effect of energy accumulation for reproduction and water temperature (quantified using SST and bottom temperature) on individual dietary specialization. Squid may increase dietary specialization as the energy demand for reproduction is closely linked to dietary selection (e.g. as has been found for Humboldt squid [57]). Reproduction is the most energy-intensive period for many marine animals, and achieving the maximum amount of energy and the optimum balance between competing demands such as activity, growth and reproduction is critical to fitness [68]. Although there is still a lack of empirical evidence for other squid species, individual dietary specialization may be an important evolutionary tactic for squids to increase reproductive fitness, particularly in the subadults and adults.

It is expected that sea surface and bottom temperature have significant effects on individual dietary specialization for the Argentine shortfin squid, with higher temperatures leading to greater dietary specialization. This relationship may be related to the amount of available energy and the rates of its acquisition and metabolic conversion [69], and result in trade-offs between net energy gain and the energetic costs of fitness-related functions such as basal metabolism and foraging [70]. Higher temperatures appear to lead to higher metabolic rates, resulting in less net energy gain [71], while the acquisition of food resources is subject to an evolutionary trade-off whereby the benefits of frequent feeding associated with large amounts of food consumed can be offset by higher predation and energy expenditure and vice versa [60,72]. Under such a trade-off, an optimal foraging strategy can be expected to evolve to maximize net energy gain, with the least expenditure through dietary specialization on high calorific prey. Indeed, the increase in δ¹⁵N values with maturation (figure 2d–e) may reflect the result of shifting diets to more calorific prey at higher trophic positions. Meanwhile, effects of water temperature on individual dietary specialization are expected given temperature variation may influence the stable isotopes and fatty acids in the environment and hence their measurements [73,74].

Ongoing climate change adds pressure for marine organisms, and is influencing species distributions, and more importantly, competition for food [75]. Predictions show that the food-web structure can be simplified by warming, leading to a shortening of the pathways of energy flux between consumers and resources [76]. Warming water temperature is also driving spatial and temporal changes in fish body size, in particular reductions in body size [77]. Consequently, high trophic level species may face a shortage of food resources, and may be forced to accept physiological modifications such as smaller size. This may lead to changed foraging habits and hence food-web truncation and ecosystem restructuring. Integrating environmental data with physiological performance when exploring dietary selection among individuals is important to make more mechanistic, individual-based predictions of foraging strategies under global climate change.

5. Conclusion

We show that dietary niche specialization among individuals is common for squid. Possible reasons are mostly related to energy acquisition, as squid can reduce interindividual competition for food by specializing. These findings increase our understanding of squid life history, providing an insight into their energy acquisition strategy during maturation. In addition, our work contributes to the application of stable isotope and fatty acid analyses in trophic ecology by quantifying the dietary differences among individuals. Furthermore, the estimation of individual dietary niches as hypervolumes extends the framework of Eltonian niche axes [12], allowing direct examination of individual niches in resource use, which aids the interpretation of individual foraging behaviour. Future studies can use this concept to assess the degree and causes of interpopulation and even interspecies dietary selection.

Ethics

Specimens of Argentine shortfin squid were collected as dead squids from the commercial jigging fisheries landings during a fishing season. The specimens were analysed in the laboratory using methods that are in line with the current Chinese national standards of Laboratory Animals - General Requirements for Animal Experiment (GB/T 35823-2018). As all material sampled in this work was obtained from commercial jigging fisheries landings and already dead, there was no requirement for ethical approval of sampling protocols as it did not include live organisms.

Data accessibility

The data and code that support the findings of this study are available from Zenodo [78].

Electronic supplementary material is available online [79].

Declaration of AI use

We have not used AI-assisted technologies in creating this article.

Authors’ contributions

D.L.: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, validation, visualization, writing—original draft, writing—review and editing; N.Z.: formal analysis, investigation; W.S.: formal analysis, investigation; A.E.P.: conceptualization, visualization, writing—review and editing; Y.G.: formal analysis, investigation; G.L.: conceptualization, visualization, writing—review and editing; X.C.: conceptualization, funding acquisition, writing—review and editing.

All authors gave final approval for publication and agreed to be held accountable for the work performed therein.

Conflict of interest declaration

We declare we have no competing interests.

Funding

This work was supported by National Natural Science Foundation of China (41876144), Shanghai Talent Development Funding (2020107) and Project on the Survey and Monitor Evaluation of Global Fishery Resources (Comprehensive Scientific Survey of Fishery Resources at the High Seas to D.L.) and National Natural Science Foundation of China (41876141) and Shanghai Science and Technology Innovation Program (19DZ1207502 to X.C.).